AbstractThis is a first step toward the goal of finding a way to calculate a smallest norm deregularizing perturbation of a given square matrix pencil. Minimal de-regularizing perturbations have geometric characterizations that include a variable projection linear least squares problem and a minimax characterization reminiscent of the Courant-Fischer theorem. The characterizations lead to new, computationally attractive upper and lower bounds. We give a brief survey and illustrate strengths and weaknesses of several upper and lower bounds some of which are well-known and some of which are new. The ultimate goal remains elusive
Many applications require recovering a matrix of minimal rank within an affine constraint set, with ...
When a matrix A is square with full rank, there is a vector x that satisfies the equation Ax=b for a...
International audienceWe consider a reformulation of Reduced-Rank Regression (RRR) and Sparse Reduce...
AbstractThis is a first step toward the goal of finding a way to calculate a smallest norm deregular...
This paper considers the problem of recovering either a low rank matrix or a sparse vector from obse...
Abstract. Matrix perturbation inequalities, such as Weyl’s theorem (con-cerning the singular values)...
In this thesis we study the eigenvalues of linear matrix pencils and their behavior under perturbati...
Perturbations of spectral projectors generated by linear matrix pencils are investigated. Estimates ...
Sparse representation and low-rank approximation are fundamental tools in fields of signal processin...
[EN] We solve the problem of determining the Weierstrass structure of a regular matrix pencil obtain...
AbstractBeauwens' procedure for obtaining lower eigenvalue bounds for (regular) pencils of matrices ...
AbstractWe establish a minimax characterization for extreme real eigenvalues of a general hermitian ...
We show that the Closest Vector Problem with Preprocessing over ` ∞ norm (CVPP∞) is NP-hard to appro...
This paper describes structural properties of solutions to distance problems for rectangular matrix ...
We present a new diagonal balancing technique for regular matrix pencils lambda B - A, which aims at...
Many applications require recovering a matrix of minimal rank within an affine constraint set, with ...
When a matrix A is square with full rank, there is a vector x that satisfies the equation Ax=b for a...
International audienceWe consider a reformulation of Reduced-Rank Regression (RRR) and Sparse Reduce...
AbstractThis is a first step toward the goal of finding a way to calculate a smallest norm deregular...
This paper considers the problem of recovering either a low rank matrix or a sparse vector from obse...
Abstract. Matrix perturbation inequalities, such as Weyl’s theorem (con-cerning the singular values)...
In this thesis we study the eigenvalues of linear matrix pencils and their behavior under perturbati...
Perturbations of spectral projectors generated by linear matrix pencils are investigated. Estimates ...
Sparse representation and low-rank approximation are fundamental tools in fields of signal processin...
[EN] We solve the problem of determining the Weierstrass structure of a regular matrix pencil obtain...
AbstractBeauwens' procedure for obtaining lower eigenvalue bounds for (regular) pencils of matrices ...
AbstractWe establish a minimax characterization for extreme real eigenvalues of a general hermitian ...
We show that the Closest Vector Problem with Preprocessing over ` ∞ norm (CVPP∞) is NP-hard to appro...
This paper describes structural properties of solutions to distance problems for rectangular matrix ...
We present a new diagonal balancing technique for regular matrix pencils lambda B - A, which aims at...
Many applications require recovering a matrix of minimal rank within an affine constraint set, with ...
When a matrix A is square with full rank, there is a vector x that satisfies the equation Ax=b for a...
International audienceWe consider a reformulation of Reduced-Rank Regression (RRR) and Sparse Reduce...